Data Sources

For weather data…

For health outcomes data, we are interested in the incidence of asthma, skin cancer and lung cancer across time and space. We obtained age-adjusted incidence rates data for these three health outcomes for the U.S. states New York, Ohio and Pennsylvania over multiple years. The following bullet points provide links from where we obtained the data.

We also obtained age-adjusted incidence rates data for these three health outcomes for the U.S. states New York, Ohio and Pennsylvania at the county-level at fixed time points. The following bullet points provide links from where we obtained the data. The asthma data is provided for the year 2016 and the skin cancer and lung cancer data is averaged over the years 2014-2018.

  • Asthma
    • Provide source
  • Melanoma of the skin (skin cancer) and lung cancer

Each of these data sets were exported and each imported into R. For each of the three health outcomes, the data sets were merged across the three states for the longitudinal data and for the cross-sectional data. This effort resulted in six data sets. The data import and cleaning of the original data can be found in the data import folder on our github repository.

We will now import the six data sets and merge all longitudinal data together and merge all cross-sectional data together to have two data sets.

Combining longitudinal state-level health outcome data

First, let’s combine the longitudinal lung and skin cancer data at the state level.

# read in longitudinal state data for lung and skin cancer

# longitudinal state level lung cancer data
state_lc_data = read_csv("./data/lung/state_lc_data.csv")

# longitudinal state level melanoma data
state_mel_data = read_csv("./data/incidence_melanoma_data/state_melanoma_age_adjusted.csv") %>%
  rename(c("outcome" = "cancer_type"))

# combine cancer outcome data
lc_mel_state = bind_rows(state_lc_data, state_mel_data) %>%
  select(state, year, outcome, age_adjusted_incidence_rate)

Next, let’s combine the longitudinal asthma data with the longitudinal cancer data at the state level.

We begin by reading in the longitudinal state-level asthma data.

# Read in longitudinal state-level asthma data
# Rename and select columns
asthma_state <- read_csv(here::here("data", "asthma_state.csv")) %>%
  rename(age_adjusted_incidence_rate = rate) %>%
  select(state, year, outcome, age_adjusted_incidence_rate)

Next, we can combine the longitudinal asthma and the longitudinal cancer state-level data.

# Bind rows of longitudinal asthma and longitudinal cancer by state and arrange 
# accordingly
lc_mel_asthma_state <- bind_rows(lc_mel_state, asthma_state) %>%
  arrange(state, year, outcome)

Lastly, let’s write out the combined data.

write_csv(lc_mel_asthma_state, here::here("data", "lc_mel_asthma_state.csv"))

Combining cross-sectional county-level health outcome data

First, let’s combine the cross-sectional lung and skin cancer data at the county level. We will add a column here for the county FIPS code for mapping purposes.

# read in cross-sectional county data for lung and skin cancer

county_lc = read_csv("./data/lung/county_lc.csv")
county_mel = read_csv("./data/incidence_melanoma_data/state_county_melanoma_incidence_2014_2018.csv")

fips_codes = read_csv("./data/fips_codes.csv")

# cleaning to make the data sets compatible

county_mel = county_mel %>%
  rename(c("outcome" = "cancer_type")) %>%
  drop_na() 

county_mel <- county_mel[!(county_mel$county == "Ohio" | county_mel$county == "Pennsylvania" | 
     county_mel$county == "New York"), ] %>%
  filter(!grepl('SEER', county))

county_mel$county <- gsub(" County","", county_mel$county)

county_mel$county[county_mel$county == "St Lawrence"] <- "St. Lawrence"

county_lc$county <- gsub(" County","", county_lc$county)

# binding rows

lc_mel_county = bind_rows(county_lc, county_mel) %>%
  filter(complete.cases(.)) 

# Add FIPS column to the data
fips = fips_codes %>%
    janitor::clean_names() %>%
    rename(c("county" = "name"))

lc_mel_county = merge(lc_mel_county, fips, by = c("state", "county"))

Next, we will read in the cross-sectional asthma county data and combine it with the cross-sectional cancer data.

We begin by reading in the cross-sectional asthma county data, renaming column names and adding a column for county FIPS code.

# Read in cross-sectional asthma data
# Only get county data for the year 2016 since we need county data at
# a fixed time point (i.e. not longitudinal)
# Adjust column names and types in prep for merge
asthma_county <- read_csv(here::here("data", "asthma_county.csv")) %>%
  filter(year == 2016) %>%
  select(-year) %>%
  rename(age_adjusted_incidence_rate = rate) %>%
  mutate(age_adjusted_incidence_rate = as.double(age_adjusted_incidence_rate))

asthma_county <- merge(asthma_county, fips, by = c("state", "county"))

Next, let’s combine the cross-sectional asthma and cancer county data.

# Bind rows of cancer and asthma by county and arrange accordingly
lc_mel_asthma_county <- bind_rows(lc_mel_county, asthma_county) %>%
  arrange(state, county, outcome)

Lastly, let’s write out the combined data.

write_csv(lc_mel_asthma_county, here::here("data", "lc_mel_asthma_county.csv"))

Now, that we have combined the health outcomes data, we can explore.

Let’s talk health outcomes:

To discuss how climate change has impacted the rate of certain negative health outcomes, let’s first assess their general prevalence.